Integration of matched filtering within the RF-DNA fingerprinting process

Radio-Frequency Distinct Native Attributes (RFDNA) fingerprinting is a Specific Emitter Identification (SEI) approach developed as a mechanism for enhancing wireless network security. RF-DNA fingerprinting exploits unintentional and distinctively unique Physical (PHY) Layer characteristics that are imparted upon the waveform during its generation and transmission. The RF-DNA fingerprinting approach specifically leverages those PHY Layer characteristics that color a fixed, known sequence of waveform symbols (e.g., IEEE 802.11a preamble). This makes the RF-DNA fingerprinting process well suited to matched filter (MF) integration, because (i) both are generated from a fixed sequence and (ii) the MF maximizes SNR while RF-DNA based radio identification performance is degraded as SNR decreases. In this work, the MF is applied prior to signal transformation, which results in four RF-DNA fingerprint generation scenarios: Fast Fourier Transform (FFT) with a MF (FFT-MF), FFT with an All-Pass Filter (FFT- APF), Gabor Transform with a MF (GT-MF), and GT with an APF (GT-APF). Performance of these four scenarios is assessed using average percent correct classification over degrading signal- to-noise channel conditions. When considering classification performance and IoT device constraints (e.g., memory, computation resources and time), RF-DNA fingerprints generated using the FFT-MF scenario proved superior to the other three.